Overview

Dataset statistics

Number of variables19
Number of observations13344
Missing cells3212
Missing cells (%)1.3%
Total size in memory1.9 MiB
Average record size in memory152.0 B

Variable types

Text17
Numeric2

Variable descriptions

BrandName of the car manufacturer
YearYear of manufacture or release
ModelName or code of the car model
Car/SuvType of the car (car or suv)
TitleTitle or description of the car
Used0rNewCondition of the car (used or new)
TransmissionType of transmission (manual or automatic)
EngineEngine capacity or power (in litres or kilowatts)
DriveTypeType of drive (front-wheel, rear-wheel, or all-wheel)
FuelTypeType of fuel (petrol, diesel, hybrid, or electric)
FuelConsumptionFuel consumption rate (in litres per N0 km)
KilometresDistance travelled by the car (in kilometres)
ColourExtIntColour of the car (exterior and interior)
LocationLocation of the car (city and state)
CylindersinEngineNumber of cylinders in the engine
BodyTypeShape or style of the car body (sedan, hatchback, coupe, etc.)
DoorsNumber of doors in the car
SeatsNumber of seats in the car
PricePrice of the car (in dollars)

Alerts

Location has 357 (2.7%) missing valuesMissing
BodyType has 230 (1.7%) missing valuesMissing
Doors has 1259 (9.4%) missing valuesMissing
Seats has 1344 (10.1%) missing valuesMissing

Reproduction

Analysis started2025-04-08 23:32:36.384954
Analysis finished2025-04-08 23:32:44.677908
Duration8.29 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

Brand
Text

Name of the car manufacturer

Distinct75
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:32:45.019626image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length10
Mean length6.058827938
Min length2

Characters and Unicode

Total characters80849
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.1%

Sample

1st rowMitsubishi
2nd rowHonda
3rd rowHonda
4th rowToyota
5th rowMercedes-Benz
ValueCountFrequency (%)
toyota 2262
17.0%
mazda 941
 
7.1%
hyundai 938
 
7.0%
holden 868
 
6.5%
ford 852
 
6.4%
nissan 849
 
6.4%
mitsubishi 816
 
6.1%
volkswagen 730
 
5.5%
kia 642
 
4.8%
mercedes-benz 496
 
3.7%
Other values (65) 3950
29.6%
2025-04-09T02:32:45.945608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 8826
 
10.9%
o 7828
 
9.7%
i 5727
 
7.1%
s 5082
 
6.3%
d 5047
 
6.2%
e 4765
 
5.9%
n 4751
 
5.9%
u 4203
 
5.2%
t 3392
 
4.2%
y 3287
 
4.1%
Other values (36) 27941
34.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 80849
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8826
 
10.9%
o 7828
 
9.7%
i 5727
 
7.1%
s 5082
 
6.3%
d 5047
 
6.2%
e 4765
 
5.9%
n 4751
 
5.9%
u 4203
 
5.2%
t 3392
 
4.2%
y 3287
 
4.1%
Other values (36) 27941
34.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 80849
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8826
 
10.9%
o 7828
 
9.7%
i 5727
 
7.1%
s 5082
 
6.3%
d 5047
 
6.2%
e 4765
 
5.9%
n 4751
 
5.9%
u 4203
 
5.2%
t 3392
 
4.2%
y 3287
 
4.1%
Other values (36) 27941
34.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 80849
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8826
 
10.9%
o 7828
 
9.7%
i 5727
 
7.1%
s 5082
 
6.3%
d 5047
 
6.2%
e 4765
 
5.9%
n 4751
 
5.9%
u 4203
 
5.2%
t 3392
 
4.2%
y 3287
 
4.1%
Other values (36) 27941
34.6%

Year
Real number (ℝ)

Year of manufacture or release

Distinct44
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.223246
Minimum1959
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-04-09T02:32:46.576859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1959
5-th percentile2006
Q12013
median2017
Q32020
95-th percentile2023
Maximum2023
Range64
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.205698722
Coefficient of variation (CV)0.002581905913
Kurtosis3.608786063
Mean2016.223246
Median Absolute Deviation (MAD)3
Skewness-1.238111753
Sum26904483
Variance27.09929918
MonotonicityNot monotonic
2025-04-09T02:32:46.977078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
2018 1256
 
9.4%
2019 1188
 
8.9%
2017 1061
 
8.0%
2022 1051
 
7.9%
2023 1032
 
7.7%
2016 944
 
7.1%
2020 895
 
6.7%
2021 890
 
6.7%
2015 876
 
6.6%
2014 697
 
5.2%
Other values (34) 3454
25.9%
ValueCountFrequency (%)
1959 1
< 0.1%
1970 1
< 0.1%
1975 1
< 0.1%
1978 1
< 0.1%
1979 1
< 0.1%
ValueCountFrequency (%)
2023 1032
7.7%
2022 1051
7.9%
2021 890
6.7%
2020 895
6.7%
2019 1188
8.9%

Model
Text

Name or code of the car model

Distinct734
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:32:47.794189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length17
Median length11
Mean length5.360461631
Min length1

Characters and Unicode

Total characters71530
Distinct characters65
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique216 ?
Unique (%)1.6%

Sample

1st rowPajero
2nd rowCR-V
3rd rowFit
4th rowHiace
5th rowCLA250
ValueCountFrequency (%)
hilux 361
 
2.7%
ranger 330
 
2.5%
corolla 319
 
2.4%
rav4 283
 
2.1%
landcruiser 282
 
2.1%
i30 279
 
2.1%
triton 237
 
1.8%
commodore 228
 
1.7%
camry 219
 
1.6%
x-trail 214
 
1.6%
Other values (720) 10592
79.4%
2025-04-09T02:32:49.057440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 6909
 
9.7%
r 6201
 
8.7%
o 4894
 
6.8%
e 4050
 
5.7%
i 3196
 
4.5%
n 3127
 
4.4%
l 3019
 
4.2%
C 2848
 
4.0%
t 2573
 
3.6%
u 1970
 
2.8%
Other values (55) 32743
45.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 71530
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 6909
 
9.7%
r 6201
 
8.7%
o 4894
 
6.8%
e 4050
 
5.7%
i 3196
 
4.5%
n 3127
 
4.4%
l 3019
 
4.2%
C 2848
 
4.0%
t 2573
 
3.6%
u 1970
 
2.8%
Other values (55) 32743
45.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 71530
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 6909
 
9.7%
r 6201
 
8.7%
o 4894
 
6.8%
e 4050
 
5.7%
i 3196
 
4.5%
n 3127
 
4.4%
l 3019
 
4.2%
C 2848
 
4.0%
t 2573
 
3.6%
u 1970
 
2.8%
Other values (55) 32743
45.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 71530
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 6909
 
9.7%
r 6201
 
8.7%
o 4894
 
6.8%
e 4050
 
5.7%
i 3196
 
4.5%
n 3127
 
4.4%
l 3019
 
4.2%
C 2848
 
4.0%
t 2573
 
3.6%
u 1970
 
2.8%
Other values (55) 32743
45.8%

Car/Suv
Text

Type of the car (car or suv)

Distinct558
Distinct (%)4.2%
Missing22
Missing (%)0.2%
Memory size104.4 KiB
2025-04-09T02:32:49.839517image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length72
Median length63
Mean length8.498198469
Min length3

Characters and Unicode

Total characters113213
Distinct characters70
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique261 ?
Unique (%)2.0%

Sample

1st rowSUV
2nd rowSUV
3rd rowUSED Dealer ad
4th rowCommercial
5th rowSedan
ValueCountFrequency (%)
suv 4709
20.5%
2047
 
8.9%
hatchback 1886
 
8.2%
ute 1685
 
7.4%
tray 1679
 
7.3%
sedan 1509
 
6.6%
new 885
 
3.9%
wagon 469
 
2.0%
used 368
 
1.6%
available 345
 
1.5%
Other values (599) 7337
32.0%
2025-04-09T02:32:51.320603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 11364
 
10.0%
9597
 
8.5%
e 7848
 
6.9%
S 6976
 
6.2%
U 6806
 
6.0%
t 5967
 
5.3%
r 5188
 
4.6%
V 4997
 
4.4%
o 4528
 
4.0%
c 4518
 
4.0%
Other values (60) 45424
40.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113213
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 11364
 
10.0%
9597
 
8.5%
e 7848
 
6.9%
S 6976
 
6.2%
U 6806
 
6.0%
t 5967
 
5.3%
r 5188
 
4.6%
V 4997
 
4.4%
o 4528
 
4.0%
c 4518
 
4.0%
Other values (60) 45424
40.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113213
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 11364
 
10.0%
9597
 
8.5%
e 7848
 
6.9%
S 6976
 
6.2%
U 6806
 
6.0%
t 5967
 
5.3%
r 5188
 
4.6%
V 4997
 
4.4%
o 4528
 
4.0%
c 4518
 
4.0%
Other values (60) 45424
40.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113213
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 11364
 
10.0%
9597
 
8.5%
e 7848
 
6.9%
S 6976
 
6.2%
U 6806
 
6.0%
t 5967
 
5.3%
r 5188
 
4.6%
V 4997
 
4.4%
o 4528
 
4.0%
c 4518
 
4.0%
Other values (60) 45424
40.1%

Title
Text

Title or description of the car

Distinct7566
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:32:52.277105image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length87
Median length74
Mean length29.56482314
Min length11

Characters and Unicode

Total characters394513
Distinct characters75
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4997 ?
Unique (%)37.4%

Sample

1st row2003 Mitsubishi Pajero Exceed LWB (4X4)
2nd row2017 Honda CR-V VTI-LX (awd)
3rd row2014 Honda Fit
4th row2010 Toyota Hiace LWB
5th row2015 Mercedes-Benz CLA250 Sport 4Matic
ValueCountFrequency (%)
4x4 2479
 
3.6%
toyota 2264
 
3.3%
2018 1257
 
1.8%
2019 1190
 
1.7%
2017 1062
 
1.5%
2022 1051
 
1.5%
2023 1032
 
1.5%
2016 945
 
1.4%
mazda 941
 
1.4%
hyundai 939
 
1.3%
Other values (2380) 56450
81.1%
2025-04-09T02:32:53.611570image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56267
 
14.3%
2 22127
 
5.6%
a 20339
 
5.2%
0 19492
 
4.9%
o 17069
 
4.3%
e 15657
 
4.0%
i 15164
 
3.8%
r 14440
 
3.7%
t 11420
 
2.9%
n 11230
 
2.8%
Other values (65) 191308
48.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 394513
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
56267
 
14.3%
2 22127
 
5.6%
a 20339
 
5.2%
0 19492
 
4.9%
o 17069
 
4.3%
e 15657
 
4.0%
i 15164
 
3.8%
r 14440
 
3.7%
t 11420
 
2.9%
n 11230
 
2.8%
Other values (65) 191308
48.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 394513
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
56267
 
14.3%
2 22127
 
5.6%
a 20339
 
5.2%
0 19492
 
4.9%
o 17069
 
4.3%
e 15657
 
4.0%
i 15164
 
3.8%
r 14440
 
3.7%
t 11420
 
2.9%
n 11230
 
2.8%
Other values (65) 191308
48.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 394513
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
56267
 
14.3%
2 22127
 
5.6%
a 20339
 
5.2%
0 19492
 
4.9%
o 17069
 
4.3%
e 15657
 
4.0%
i 15164
 
3.8%
r 14440
 
3.7%
t 11420
 
2.9%
n 11230
 
2.8%
Other values (65) 191308
48.5%
Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:32:53.932192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.928881894
Min length3

Characters and Unicode

Total characters52427
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUSED
2nd rowUSED
3rd rowUSED
4th rowUSED
5th rowUSED
ValueCountFrequency (%)
used 11982
89.8%
new 949
 
7.1%
demo 413
 
3.1%
2025-04-09T02:32:54.536284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 13344
25.5%
D 12395
23.6%
U 11982
22.9%
S 11982
22.9%
N 949
 
1.8%
W 949
 
1.8%
M 413
 
0.8%
O 413
 
0.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 52427
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 13344
25.5%
D 12395
23.6%
U 11982
22.9%
S 11982
22.9%
N 949
 
1.8%
W 949
 
1.8%
M 413
 
0.8%
O 413
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 52427
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 13344
25.5%
D 12395
23.6%
U 11982
22.9%
S 11982
22.9%
N 949
 
1.8%
W 949
 
1.8%
M 413
 
0.8%
O 413
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 52427
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 13344
25.5%
D 12395
23.6%
U 11982
22.9%
S 11982
22.9%
N 949
 
1.8%
W 949
 
1.8%
M 413
 
0.8%
O 413
 
0.8%

Transmission
Text

Type of transmission (manual or automatic)

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:32:54.858554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.53117506
Min length1

Characters and Unicode

Total characters113840
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAutomatic
2nd rowAutomatic
3rd row-
4th rowAutomatic
5th rowAutomatic
ValueCountFrequency (%)
automatic 11587
86.8%
manual 1560
 
11.7%
197
 
1.5%
2025-04-09T02:32:55.414369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 23174
20.4%
a 14707
12.9%
u 13147
11.5%
A 11587
10.2%
o 11587
10.2%
m 11587
10.2%
i 11587
10.2%
c 11587
10.2%
M 1560
 
1.4%
n 1560
 
1.4%
Other values (2) 1757
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 113840
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 23174
20.4%
a 14707
12.9%
u 13147
11.5%
A 11587
10.2%
o 11587
10.2%
m 11587
10.2%
i 11587
10.2%
c 11587
10.2%
M 1560
 
1.4%
n 1560
 
1.4%
Other values (2) 1757
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 113840
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 23174
20.4%
a 14707
12.9%
u 13147
11.5%
A 11587
10.2%
o 11587
10.2%
m 11587
10.2%
i 11587
10.2%
c 11587
10.2%
M 1560
 
1.4%
n 1560
 
1.4%
Other values (2) 1757
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 113840
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 23174
20.4%
a 14707
12.9%
u 13147
11.5%
A 11587
10.2%
o 11587
10.2%
m 11587
10.2%
i 11587
10.2%
c 11587
10.2%
M 1560
 
1.4%
n 1560
 
1.4%
Other values (2) 1757
 
1.5%

Engine
Text

Engine capacity or power (in litres or kilowatts)

Distinct102
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:32:55.900341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length12
Mean length10.1614958
Min length1

Characters and Unicode

Total characters135595
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)0.2%

Sample

1st row4 cyl, 3.2 L
2nd row4 cyl, 1.5 L
3rd row-
4th row4 cyl, 3 L
5th row4 cyl, 2 L
ValueCountFrequency (%)
l 12023
24.4%
cyl 11938
24.2%
4 9344
19.0%
2 3136
 
6.4%
6 1755
 
3.6%
2.5 1358
 
2.8%
1320
 
2.7%
3 1306
 
2.7%
2.4 683
 
1.4%
1.5 629
 
1.3%
Other values (60) 5750
11.7%
2025-04-09T02:32:56.674304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
35898
26.5%
L 12023
 
8.9%
c 11938
 
8.8%
y 11938
 
8.8%
l 11938
 
8.8%
, 11937
 
8.8%
4 10689
 
7.9%
2 7577
 
5.6%
. 7313
 
5.4%
3 2894
 
2.1%
Other values (8) 11450
 
8.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135595
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
35898
26.5%
L 12023
 
8.9%
c 11938
 
8.8%
y 11938
 
8.8%
l 11938
 
8.8%
, 11937
 
8.8%
4 10689
 
7.9%
2 7577
 
5.6%
. 7313
 
5.4%
3 2894
 
2.1%
Other values (8) 11450
 
8.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135595
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
35898
26.5%
L 12023
 
8.9%
c 11938
 
8.8%
y 11938
 
8.8%
l 11938
 
8.8%
, 11937
 
8.8%
4 10689
 
7.9%
2 7577
 
5.6%
. 7313
 
5.4%
3 2894
 
2.1%
Other values (8) 11450
 
8.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135595
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
35898
26.5%
L 12023
 
8.9%
c 11938
 
8.8%
y 11938
 
8.8%
l 11938
 
8.8%
, 11937
 
8.8%
4 10689
 
7.9%
2 7577
 
5.6%
. 7313
 
5.4%
3 2894
 
2.1%
Other values (8) 11450
 
8.4%

DriveType
Text

Type of drive (front-wheel, rear-wheel, or all-wheel)

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:32:57.012855image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.111885492
Min length3

Characters and Unicode

Total characters54869
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4WD
2nd rowAWD
3rd rowOther
4th rowRear
5th rowAWD
ValueCountFrequency (%)
front 5576
41.8%
4wd 2520
18.9%
awd 2464
18.5%
rear 1883
 
14.1%
other 901
 
6.8%
2025-04-09T02:32:57.782029image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
r 8360
15.2%
t 6477
11.8%
F 5576
10.2%
o 5576
10.2%
n 5576
10.2%
W 4984
9.1%
D 4984
9.1%
e 2784
 
5.1%
4 2520
 
4.6%
A 2464
 
4.5%
Other values (4) 5568
10.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 54869
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 8360
15.2%
t 6477
11.8%
F 5576
10.2%
o 5576
10.2%
n 5576
10.2%
W 4984
9.1%
D 4984
9.1%
e 2784
 
5.1%
4 2520
 
4.6%
A 2464
 
4.5%
Other values (4) 5568
10.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 54869
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 8360
15.2%
t 6477
11.8%
F 5576
10.2%
o 5576
10.2%
n 5576
10.2%
W 4984
9.1%
D 4984
9.1%
e 2784
 
5.1%
4 2520
 
4.6%
A 2464
 
4.5%
Other values (4) 5568
10.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 54869
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 8360
15.2%
t 6477
11.8%
F 5576
10.2%
o 5576
10.2%
n 5576
10.2%
W 4984
9.1%
D 4984
9.1%
e 2784
 
5.1%
4 2520
 
4.6%
A 2464
 
4.5%
Other values (4) 5568
10.1%

FuelType
Text

Type of fuel (petrol, diesel, hybrid, or electric)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:32:58.112136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length7
Mean length6.853267386
Min length1

Characters and Unicode

Total characters91450
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowDiesel
2nd rowUnleaded
3rd rowOther
4th rowDiesel
5th rowPremium
ValueCountFrequency (%)
unleaded 5550
41.6%
diesel 3905
29.3%
premium 2710
20.3%
hybrid 526
 
3.9%
508
 
3.8%
electric 93
 
0.7%
other 37
 
0.3%
lpg 11
 
0.1%
leaded 4
 
< 0.1%
2025-04-09T02:32:58.845771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 21758
23.8%
d 11634
12.7%
l 9548
10.4%
i 7234
 
7.9%
a 5554
 
6.1%
U 5550
 
6.1%
n 5550
 
6.1%
m 5420
 
5.9%
D 3905
 
4.3%
s 3905
 
4.3%
Other values (14) 11392
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 91450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 21758
23.8%
d 11634
12.7%
l 9548
10.4%
i 7234
 
7.9%
a 5554
 
6.1%
U 5550
 
6.1%
n 5550
 
6.1%
m 5420
 
5.9%
D 3905
 
4.3%
s 3905
 
4.3%
Other values (14) 11392
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 91450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 21758
23.8%
d 11634
12.7%
l 9548
10.4%
i 7234
 
7.9%
a 5554
 
6.1%
U 5550
 
6.1%
n 5550
 
6.1%
m 5420
 
5.9%
D 3905
 
4.3%
s 3905
 
4.3%
Other values (14) 11392
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 91450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 21758
23.8%
d 11634
12.7%
l 9548
10.4%
i 7234
 
7.9%
a 5554
 
6.1%
U 5550
 
6.1%
n 5550
 
6.1%
m 5420
 
5.9%
D 3905
 
4.3%
s 3905
 
4.3%
Other values (14) 11392
12.5%

FuelConsumption
Text

Fuel consumption rate (in litres per N0 km)

Distinct153
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:32:59.424106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length15
Median length14
Mean length12.566247
Min length1

Characters and Unicode

Total characters167684
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.2%

Sample

1st row0 L / 100 km
2nd row7.4 L / 100 km
3rd row-
4th row8 L / 100 km
5th row6.6 L / 100 km
ValueCountFrequency (%)
13344
21.7%
km 12006
19.6%
l 12006
19.6%
100 12006
19.6%
7.4 566
 
0.9%
7.9 411
 
0.7%
7.3 376
 
0.6%
8.1 361
 
0.6%
7.6 344
 
0.6%
7.5 318
 
0.5%
Other values (146) 9630
15.7%
2025-04-09T02:33:00.356023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48024
28.6%
0 24890
14.8%
1 14863
 
8.9%
L 12006
 
7.2%
/ 12006
 
7.2%
k 12006
 
7.2%
m 12006
 
7.2%
. 10429
 
6.2%
7 4093
 
2.4%
8 3385
 
2.0%
Other values (7) 13976
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 167684
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
48024
28.6%
0 24890
14.8%
1 14863
 
8.9%
L 12006
 
7.2%
/ 12006
 
7.2%
k 12006
 
7.2%
m 12006
 
7.2%
. 10429
 
6.2%
7 4093
 
2.4%
8 3385
 
2.0%
Other values (7) 13976
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 167684
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
48024
28.6%
0 24890
14.8%
1 14863
 
8.9%
L 12006
 
7.2%
/ 12006
 
7.2%
k 12006
 
7.2%
m 12006
 
7.2%
. 10429
 
6.2%
7 4093
 
2.4%
8 3385
 
2.0%
Other values (7) 13976
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 167684
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
48024
28.6%
0 24890
14.8%
1 14863
 
8.9%
L 12006
 
7.2%
/ 12006
 
7.2%
k 12006
 
7.2%
m 12006
 
7.2%
. 10429
 
6.2%
7 4093
 
2.4%
8 3385
 
2.0%
Other values (7) 13976
 
8.3%

Kilometres
Text

Distance travelled by the car (in kilometres)

Distinct11531
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:33:01.131837image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.142311151
Min length1

Characters and Unicode

Total characters68619
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10972 ?
Unique (%)82.2%

Sample

1st row343128
2nd row130813
3rd row44248
4th row147317
5th row78403
ValueCountFrequency (%)
1170
 
8.3%
10 105
 
0.7%
15 74
 
0.5%
20 72
 
0.5%
7 46
 
0.3%
22 38
 
0.3%
12 29
 
0.2%
11 28
 
0.2%
25 28
 
0.2%
5 28
 
0.2%
Other values (11520) 12440
88.5%
2025-04-09T02:33:02.576521image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10538
15.4%
0 8182
11.9%
2 7599
11.1%
5 6513
9.5%
3 5938
8.7%
4 5781
8.4%
8 5709
8.3%
6 5611
8.2%
9 5496
8.0%
7 5368
7.8%
Other values (3) 1884
 
2.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68619
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 10538
15.4%
0 8182
11.9%
2 7599
11.1%
5 6513
9.5%
3 5938
8.7%
4 5781
8.4%
8 5709
8.3%
6 5611
8.2%
9 5496
8.0%
7 5368
7.8%
Other values (3) 1884
 
2.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68619
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 10538
15.4%
0 8182
11.9%
2 7599
11.1%
5 6513
9.5%
3 5938
8.7%
4 5781
8.4%
8 5709
8.3%
6 5611
8.2%
9 5496
8.0%
7 5368
7.8%
Other values (3) 1884
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68619
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 10538
15.4%
0 8182
11.9%
2 7599
11.1%
5 6513
9.5%
3 5938
8.7%
4 5781
8.4%
8 5709
8.3%
6 5611
8.2%
9 5496
8.0%
7 5368
7.8%
Other values (3) 1884
 
2.7%

ColourExtInt
Text

Colour of the car (exterior and interior)

Distinct732
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:33:03.330750image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length77
Median length70
Mean length11.79061751
Min length5

Characters and Unicode

Total characters157334
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique483 ?
Unique (%)3.6%

Sample

1st rowSilver / Cream Leather
2nd rowBlue / Black
3rd rowBlack / Black
4th rowSilver / Grey
5th rowRed / -
ValueCountFrequency (%)
19908
47.2%
black 6023
 
14.3%
white 4780
 
11.3%
grey 3105
 
7.4%
silver 1834
 
4.3%
blue 1309
 
3.1%
red 963
 
2.3%
leather 641
 
1.5%
cloth 502
 
1.2%
years 357
 
0.8%
Other values (360) 2744
 
6.5%
2025-04-09T02:33:04.783255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
28898
18.4%
e 14887
 
9.5%
/ 13516
 
8.6%
l 10416
 
6.6%
a 7813
 
5.0%
B 7764
 
4.9%
i 7591
 
4.8%
r 6800
 
4.3%
t 6578
 
4.2%
k 6515
 
4.1%
Other values (62) 46556
29.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 157334
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
28898
18.4%
e 14887
 
9.5%
/ 13516
 
8.6%
l 10416
 
6.6%
a 7813
 
5.0%
B 7764
 
4.9%
i 7591
 
4.8%
r 6800
 
4.3%
t 6578
 
4.2%
k 6515
 
4.1%
Other values (62) 46556
29.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 157334
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
28898
18.4%
e 14887
 
9.5%
/ 13516
 
8.6%
l 10416
 
6.6%
a 7813
 
5.0%
B 7764
 
4.9%
i 7591
 
4.8%
r 6800
 
4.3%
t 6578
 
4.2%
k 6515
 
4.1%
Other values (62) 46556
29.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 157334
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
28898
18.4%
e 14887
 
9.5%
/ 13516
 
8.6%
l 10416
 
6.6%
a 7813
 
5.0%
B 7764
 
4.9%
i 7591
 
4.8%
r 6800
 
4.3%
t 6578
 
4.2%
k 6515
 
4.1%
Other values (62) 46556
29.6%

Location
Text

MISSING 

Location of the car (city and state)

Distinct610
Distinct (%)4.7%
Missing357
Missing (%)2.7%
Memory size104.4 KiB
2025-04-09T02:33:05.448030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length22
Mean length14.28312928
Min length8

Characters and Unicode

Total characters185495
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)0.4%

Sample

1st rowStanthorpe, QLD
2nd rowRavenhall, VIC
3rd rowMinchinbury, NSW
4th rowLidcombe, NSW
5th rowRozelle, NSW
ValueCountFrequency (%)
nsw 5070
 
17.3%
vic 3096
 
10.6%
qld 2219
 
7.6%
wa 1427
 
4.9%
sa 616
 
2.1%
park 508
 
1.7%
minchinbury 426
 
1.5%
act 319
 
1.1%
blacktown 241
 
0.8%
south 238
 
0.8%
Other values (591) 15084
51.6%
2025-04-09T02:33:06.433331image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16257
 
8.8%
, 12987
 
7.0%
o 10242
 
5.5%
e 10017
 
5.4%
a 9962
 
5.4%
r 9660
 
5.2%
n 9388
 
5.1%
W 7508
 
4.0%
l 7453
 
4.0%
i 7292
 
3.9%
Other values (44) 84729
45.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 185495
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
16257
 
8.8%
, 12987
 
7.0%
o 10242
 
5.5%
e 10017
 
5.4%
a 9962
 
5.4%
r 9660
 
5.2%
n 9388
 
5.1%
W 7508
 
4.0%
l 7453
 
4.0%
i 7292
 
3.9%
Other values (44) 84729
45.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 185495
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
16257
 
8.8%
, 12987
 
7.0%
o 10242
 
5.5%
e 10017
 
5.4%
a 9962
 
5.4%
r 9660
 
5.2%
n 9388
 
5.1%
W 7508
 
4.0%
l 7453
 
4.0%
i 7292
 
3.9%
Other values (44) 84729
45.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 185495
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
16257
 
8.8%
, 12987
 
7.0%
o 10242
 
5.5%
e 10017
 
5.4%
a 9962
 
5.4%
r 9660
 
5.2%
n 9388
 
5.1%
W 7508
 
4.0%
l 7453
 
4.0%
i 7292
 
3.9%
Other values (44) 84729
45.7%

CylindersinEngine
Text

Number of cylinders in the engine

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size104.4 KiB
2025-04-09T02:33:06.779082image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length6
Median length5
Mean length4.592625899
Min length1

Characters and Unicode

Total characters61284
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row4 cyl
2nd row4 cyl
3rd row-
4th row4 cyl
5th row4 cyl
ValueCountFrequency (%)
cyl 11938
47.1%
4 9158
36.1%
6 1687
 
6.7%
1320
 
5.2%
8 484
 
1.9%
5 318
 
1.3%
3 272
 
1.1%
l 86
 
0.3%
0 85
 
0.3%
12 14
 
0.1%
Other values (2) 6
 
< 0.1%
2025-04-09T02:33:07.293708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12024
19.6%
c 11938
19.5%
y 11938
19.5%
l 11938
19.5%
4 9158
14.9%
6 1687
 
2.8%
- 1320
 
2.2%
8 484
 
0.8%
5 318
 
0.5%
3 272
 
0.4%
Other values (4) 207
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 61284
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12024
19.6%
c 11938
19.5%
y 11938
19.5%
l 11938
19.5%
4 9158
14.9%
6 1687
 
2.8%
- 1320
 
2.2%
8 484
 
0.8%
5 318
 
0.5%
3 272
 
0.4%
Other values (4) 207
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 61284
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12024
19.6%
c 11938
19.5%
y 11938
19.5%
l 11938
19.5%
4 9158
14.9%
6 1687
 
2.8%
- 1320
 
2.2%
8 484
 
0.8%
5 318
 
0.5%
3 272
 
0.4%
Other values (4) 207
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 61284
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12024
19.6%
c 11938
19.5%
y 11938
19.5%
l 11938
19.5%
4 9158
14.9%
6 1687
 
2.8%
- 1320
 
2.2%
8 484
 
0.8%
5 318
 
0.5%
3 272
 
0.4%
Other values (4) 207
 
0.3%

BodyType
Text

MISSING 

Shape or style of the car body (sedan, hatchback, coupe, etc.)

Distinct10
Distinct (%)0.1%
Missing230
Missing (%)1.7%
Memory size104.4 KiB
2025-04-09T02:33:07.566399image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length12
Median length11
Mean length5.828580143
Min length3

Characters and Unicode

Total characters76436
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSUV
2nd rowSUV
3rd rowHatchback
4th rowCommercial
5th rowSedan
ValueCountFrequency (%)
suv 5487
32.0%
hatchback 2138
 
12.5%
ute 2019
 
11.8%
2019
 
11.8%
tray 2019
 
11.8%
sedan 1577
 
9.2%
wagon 992
 
5.8%
commercial 498
 
2.9%
coupe 272
 
1.6%
convertible 95
 
0.6%
Other values (3) 55
 
0.3%
2025-04-09T02:33:08.168092image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 9362
 
12.2%
U 7506
 
9.8%
S 7064
 
9.2%
V 5487
 
7.2%
c 4774
 
6.2%
e 4630
 
6.1%
t 4269
 
5.6%
4057
 
5.3%
n 2664
 
3.5%
r 2648
 
3.5%
Other values (21) 23975
31.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 76436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 9362
 
12.2%
U 7506
 
9.8%
S 7064
 
9.2%
V 5487
 
7.2%
c 4774
 
6.2%
e 4630
 
6.1%
t 4269
 
5.6%
4057
 
5.3%
n 2664
 
3.5%
r 2648
 
3.5%
Other values (21) 23975
31.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 76436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 9362
 
12.2%
U 7506
 
9.8%
S 7064
 
9.2%
V 5487
 
7.2%
c 4774
 
6.2%
e 4630
 
6.1%
t 4269
 
5.6%
4057
 
5.3%
n 2664
 
3.5%
r 2648
 
3.5%
Other values (21) 23975
31.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 76436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 9362
 
12.2%
U 7506
 
9.8%
S 7064
 
9.2%
V 5487
 
7.2%
c 4774
 
6.2%
e 4630
 
6.1%
t 4269
 
5.6%
4057
 
5.3%
n 2664
 
3.5%
r 2648
 
3.5%
Other values (21) 23975
31.4%

Doors
Text

MISSING 

Number of doors in the car

Distinct13
Distinct (%)0.1%
Missing1259
Missing (%)9.4%
Memory size104.4 KiB
2025-04-09T02:33:08.453770image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.000082747
Min length8

Characters and Unicode

Total characters96681
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row 4 Doors
2nd row 4 Doors
3rd row 4 Doors
4th row 4 Doors
5th row 2 Doors
ValueCountFrequency (%)
doors 12024
49.7%
4 8905
36.8%
5 2053
 
8.5%
2 919
 
3.8%
3 182
 
0.8%
seats 61
 
0.3%
6 9
 
< 0.1%
8 7
 
< 0.1%
7 7
 
< 0.1%
9 2
 
< 0.1%
2025-04-09T02:33:09.120877image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24170
25.0%
o 24048
24.9%
s 12085
12.5%
D 12024
12.4%
r 12024
12.4%
4 8905
 
9.2%
5 2053
 
2.1%
2 920
 
1.0%
3 182
 
0.2%
a 61
 
0.1%
Other values (8) 209
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96681
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
24170
25.0%
o 24048
24.9%
s 12085
12.5%
D 12024
12.4%
r 12024
12.4%
4 8905
 
9.2%
5 2053
 
2.1%
2 920
 
1.0%
3 182
 
0.2%
a 61
 
0.1%
Other values (8) 209
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96681
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
24170
25.0%
o 24048
24.9%
s 12085
12.5%
D 12024
12.4%
r 12024
12.4%
4 8905
 
9.2%
5 2053
 
2.1%
2 920
 
1.0%
3 182
 
0.2%
a 61
 
0.1%
Other values (8) 209
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96681
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
24170
25.0%
o 24048
24.9%
s 12085
12.5%
D 12024
12.4%
r 12024
12.4%
4 8905
 
9.2%
5 2053
 
2.1%
2 920
 
1.0%
3 182
 
0.2%
a 61
 
0.1%
Other values (8) 209
 
0.2%

Seats
Text

MISSING 

Number of seats in the car

Distinct12
Distinct (%)0.1%
Missing1344
Missing (%)10.1%
Memory size104.4 KiB
2025-04-09T02:33:09.474429image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length9
Median length8
Mean length8.001583333
Min length8

Characters and Unicode

Total characters96019
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row 7 Seats
2nd row 5 Seats
3rd row 3 Seats
4th row 5 Seats
5th row 2 Seats
ValueCountFrequency (%)
seats 12000
50.0%
5 9032
37.6%
7 1412
 
5.9%
4 537
 
2.2%
2 485
 
2.0%
3 267
 
1.1%
8 222
 
0.9%
6 23
 
0.1%
14 11
 
< 0.1%
9 3
 
< 0.1%
Other values (3) 8
 
< 0.1%
2025-04-09T02:33:10.214372image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24000
25.0%
S 12000
12.5%
e 12000
12.5%
a 12000
12.5%
t 12000
12.5%
s 12000
12.5%
5 9032
 
9.4%
7 1412
 
1.5%
4 548
 
0.6%
2 492
 
0.5%
Other values (5) 535
 
0.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 96019
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
24000
25.0%
S 12000
12.5%
e 12000
12.5%
a 12000
12.5%
t 12000
12.5%
s 12000
12.5%
5 9032
 
9.4%
7 1412
 
1.5%
4 548
 
0.6%
2 492
 
0.5%
Other values (5) 535
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 96019
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
24000
25.0%
S 12000
12.5%
e 12000
12.5%
a 12000
12.5%
t 12000
12.5%
s 12000
12.5%
5 9032
 
9.4%
7 1412
 
1.5%
4 548
 
0.6%
2 492
 
0.5%
Other values (5) 535
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 96019
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
24000
25.0%
S 12000
12.5%
e 12000
12.5%
a 12000
12.5%
t 12000
12.5%
s 12000
12.5%
5 9032
 
9.4%
7 1412
 
1.5%
4 548
 
0.6%
2 492
 
0.5%
Other values (5) 535
 
0.6%

Price
Real number (ℝ)

Price of the car (in dollars)

Distinct3316
Distinct (%)24.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37381.70811
Minimum88
Maximum1500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size104.4 KiB
2025-04-09T02:33:10.677485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum88
5-th percentile9306.5
Q118999
median29789.5
Q343990
95-th percentile85868
Maximum1500000
Range1499912
Interquartile range (IQR)24991

Descriptive statistics

Standard deviation37707.032
Coefficient of variation (CV)1.008702756
Kurtosis215.3432384
Mean37381.70811
Median Absolute Deviation (MAD)11894.5
Skewness9.265098557
Sum498821513
Variance1421820262
MonotonicityNot monotonic
2025-04-09T02:33:11.124441image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29990 194
 
1.5%
19990 190
 
1.4%
24990 169
 
1.3%
39990 146
 
1.1%
28990 140
 
1.0%
26990 139
 
1.0%
21990 137
 
1.0%
22990 136
 
1.0%
34990 134
 
1.0%
23990 134
 
1.0%
Other values (3306) 11825
88.6%
ValueCountFrequency (%)
88 1
< 0.1%
900 1
< 0.1%
1200 1
< 0.1%
1895 2
< 0.1%
2050 1
< 0.1%
ValueCountFrequency (%)
1500000 1
< 0.1%
649880 2
< 0.1%
610000 1
< 0.1%
579888 1
< 0.1%
553600 1
< 0.1%

Correlations

2025-04-09T02:33:11.480304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
PriceYear
Price1.0000.711
Year0.7111.000
2025-04-09T02:33:11.993246image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
BrandYearUsedOrNewTransmissionDriveTypeFuelTypeCylindersinEngineBodyTypeDoorsSeatsPrice
Brand1.0000.6020.6520.3730.7340.7080.7500.6580.6860.5510.772
Year0.6021.0000.3330.2570.2750.5440.2120.2060.1880.1350.685
UsedOrNew0.6520.3331.0000.2360.0840.3510.1590.1950.1120.2190.255
Transmission0.3730.2570.2361.0000.3410.5130.3620.3960.3230.4160.093
DriveType0.7340.2750.0840.3411.0000.5810.6830.8250.6330.4300.106
FuelType0.7080.5440.3510.5130.5811.0000.6670.4790.4600.2950.129
CylindersinEngine0.7500.2120.1590.3620.6830.6671.0000.4590.6560.2100.413
BodyType0.6580.2060.1950.3960.8250.4790.4591.0000.6910.7870.193
Doors0.6860.1880.1120.3230.6330.4600.6560.6911.0000.8280.152
Seats0.5510.1350.2190.4160.4300.2950.2100.7870.8281.0000.168
Price0.7720.6850.2550.0930.1060.1290.4130.1930.1520.1681.000

Missing values

2025-04-09T02:32:43.165903image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-04-09T02:32:44.393351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.